Machine Learning Methods for Multi-Step Traffic Acquisition


Machine Learning Methods for Multi-Step Traffic Acquisition – Sparse-time classification (STR) has emerged as a promising tool for automatic vehicle identification. The main drawback of STR is its lack of training data and the difficulty of handling noisy data. In this work we present an innovative approach to the problem using Convolutional Neural Networks. In our model, we first use unsupervised learning as feature representation for image classification: the Convolutional Neural Network (CNN) is trained with an unlabeled image. The CNN learns a binary metric feature embedding representation of its output vectors (e.g., the k-dimensional). Following this representation, the CNN can model the training data by selecting a high-quality subset of the training data. Our method learns the representations and, by using the learned representations, can be used with the standard segmentation and classification algorithms in order to learn the feature representation for the given dataset. We evaluate our method on the challenging TIDA dataset and compare it to the state-of-the-arts.

This paper presents a general framework for automatic decision making in the context of decision making in dynamic decision contexts. We formalise decision making as a set of distributed decision processes where the agents form their opinions and the actions taken are based on the decision process rules governing the decisions. We apply this framework to a variety of decision processes of non-smooth decision making as well as to decision and resource allocation.

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Machine Learning Methods for Multi-Step Traffic Acquisition

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  • Mixture-of-Parents clustering for causal inference based on incomplete observations

    Generalist probability theory and dynamic decision support systemsThis paper presents a general framework for automatic decision making in the context of decision making in dynamic decision contexts. We formalise decision making as a set of distributed decision processes where the agents form their opinions and the actions taken are based on the decision process rules governing the decisions. We apply this framework to a variety of decision processes of non-smooth decision making as well as to decision and resource allocation.


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